Generalized Linear Models

class pymc3.glm.linear.LinearComponent(x, y, intercept=True, labels=None, priors=None, vars=None, name='', model=None, offset=0.0)

Creates linear component, y_est is accessible via attribute

Parameters:

name : str - name, associated with the linear component

x : pd.DataFrame or np.ndarray

y : pd.Series or np.array

intercept : bool - fit with intercept or not?

labels : list - replace variable names with these labels

priors : dict - priors for coefficients

use Intercept key for defining Intercept prior

defaults to Flat.dist()

use Regressor key for defining default prior for all regressors

defaults to Normal.dist(mu=0, tau=1.0E-6)

vars : dict - random variables instead of creating new ones

offset : scalar, or numpy/theano array with the same shape as y

this can be used to specify an a priori known component to be included in the linear predictor during fitting.

class pymc3.glm.linear.GLM(x, y, intercept=True, labels=None, priors=None, vars=None, family='normal', name='', model=None, offset=0.0)

Creates glm model, y_est is accessible via attribute

Parameters:

name : str - name, associated with the linear component

x : pd.DataFrame or np.ndarray

y : pd.Series or np.array

intercept : bool - fit with intercept or not?

labels : list - replace variable names with these labels

priors : dict - priors for coefficients

use Intercept key for defining Intercept prior

defaults to Flat.dist()

use Regressor key for defining default prior for all regressors

defaults to Normal.dist(mu=0, tau=1.0E-6)

init : dict - test_vals for coefficients

vars : dict - random variables instead of creating new ones

family : pymc3..families object

offset : scalar, or numpy/theano array with the same shape as y

this can be used to specify an a priori known component to be included in the linear predictor during fitting.